# PDF to Excel Table Extractor (`tempting_adzuki/pdf-to-excel`) Actor

Extract structured tables from PDF files and convert them into clean, formatted Excel (.xlsx) workbooks automatically.

- **URL**: https://apify.com/tempting\_adzuki/pdf-to-excel.md
- **Developed by:** [Daniel Messias Leão da silva](https://apify.com/tempting_adzuki) (community)
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$50.00 / 1,000 successful pdf to excel conversions

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## PDF to Excel Table Extractor

Convert structured tables from PDF files into clean, formatted Excel (.xlsx) workbooks automatically.

### What it does

This Actor downloads a public PDF, detects structured tables, extracts their data, and creates an Excel workbook ready for review, sorting, filtering, or further processing.

#### Features

- Extracts tables from PDF files
- Creates Excel `.xlsx` output
- Preserves numeric values when possible
- Formats headers and numeric columns
- Freezes the header row
- Adds Excel filters
- Performs extraction quality checks
- Charges only when at least one table is successfully extracted

### Input

Provide a public HTTPS URL pointing to a PDF file.

Example:

````json
{
  "pdf_url": "https://example.com/document.pdf"
}

### Output

A successful run returns the number of tables and rows extracted, the extraction method, quality scores, and the generated Excel file.

The generated workbook is stored as:

OUTPUT.xlsx

### Pricing

You are charged only when at least one table is successfully extracted.

Current price: $0.05 per successful conversion.

PDFs where no table is detected are not charged the conversion event.

### Notes

- The PDF URL must be publicly accessible through HTTPS.
- Results depend on the structure and quality of the source PDF.
- Scanned or image-only PDFs may require OCR.



# Actor input Schema

## `pdf_url` (type: `string`):

Public HTTPS URL of the PDF to process.

## Actor input object example

```json
{
  "pdf_url": "https://raw.githubusercontent.com/pdfplum/pdfplum/main/template-samples/invoices/invoice-1.pdf"
}
````

# Actor output Schema

## `results` (type: `string`):

No description

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "pdf_url": "https://raw.githubusercontent.com/pdfplum/pdfplum/main/template-samples/invoices/invoice-1.pdf"
};

// Run the Actor and wait for it to finish
const run = await client.actor("tempting_adzuki/pdf-to-excel").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "pdf_url": "https://raw.githubusercontent.com/pdfplum/pdfplum/main/template-samples/invoices/invoice-1.pdf" }

# Run the Actor and wait for it to finish
run = client.actor("tempting_adzuki/pdf-to-excel").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "pdf_url": "https://raw.githubusercontent.com/pdfplum/pdfplum/main/template-samples/invoices/invoice-1.pdf"
}' |
apify call tempting_adzuki/pdf-to-excel --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,tempting_adzuki/pdf-to-excel"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/n29O6oLusLa0xeepe/builds/BZ24kQuelxq7vM3mg/openapi.json